The two-part special, 'The Amazon Review Killer,' is now streaming on Fox Nation. Amazon already dominates online shopping, but now it's setting its sights even higher. With a new artificial intelligence-powered project called Starfish, the company aims to become the world's most complete and trusted source of product information. The goal? Make every listing on Amazon accurate, detailed and easy to understand, whether the product is sold by Amazon or a third-party seller. If the project works as planned, it could save sellers hours of work and help shoppers find what they need faster.

Text plagiarism detection task is a common natural language processing task that aims to detect whether a given text contains plagiarism or copying from other texts. In existing research, detection of high level plagiarism is still a challenge due to the lack of high quality datasets. In this paper, we propose a plagiarized text data generation method based on GPT-3.5, which produces 32,927 pairs of text plagiarism detection datasets covering a wide range of plagiarism methods, bridging the gap in this part of research. Meanwhile, we propose a plagiarism identification method based on Faiss with BERT with high efficiency and high accuracy. Our experiments show that the performance of this model outperforms other models in several metrics, including 98.86\%, 98.90%, 98.86%, and 0.9888 for Accuracy, Precision, Recall, and F1 Score, respectively. At the end, we also provide a user-friendly demo platform that allows users to upload a text library and intuitively participate in the plagiarism analysis.

With the big popularity and success of Judea Pearl's original causality book, this review covers the main topics updated in the second edition in 2009 and illustrates an easy-to-follow causal inference strategy in a forecast scenario. It further discusses some potential benefits and challenges for causal inference with time series forecasting when modeling the counterfactuals, estimating the uncertainty and incorporating prior knowledge to estimate causal effects in different forecasting scenarios.

BetterUp, the human transformation company,announced it has launched BetterUp Sales Performance on Salesforce AppExchange, empowering customers to reach peak performance through dedicated professional sales coaching and measure the impact coaching is having on business outcomes including quota attainment, deal velocity, time to productivity, and more. "BetterUp's Sales Performance is a welcome addition to AppExchange as it powers transformation for customers by unlocking team performance across sales organizations" Integrated directly with Salesforce, BetterUp's Sales Performance is currently available on AppExchange. Additionally, BetterUp and Salesforce customers will soon have access to a dashboard embedded into Salesforce that measures revenue-related metrics driven by coaches and non-coached sales representatives. "Salesforce is one of the first adopters of the Human Transformation Platform and remains committed to the importance of mental fitness, inclusion and connection," said Alexi Robichaux, CEO and co-founder of BetterUp. "With our expanded partnership, sales managers and sellers using Sales Cloud will now have support to strategize on deals, prep for prospect calls, and develop the right mindsets around resilience, focus, and agility. We're thrilled to bring this solution to Salesforce customers to help them achieve and exceed sales performance."

Measuring sales performance is a crucial aspect of running a successful business. Accurately tracking and analyzing sales data helps companies understand their strengths and weaknesses, perform forecasts, identify trends, and make informed decisions that drive growth. In this article, I will illuminate how some simple statistical models can be used for measuring sales performance. Whether it is a small or enterprise sales team, simple quantitative techniques can be used to provide valuable sales insights or draw attention to areas of need. After reading this article, you will see various examples how simple models are applied in real life scenarios. Note: All the images in the article were generated by Artificial Intelligence using Stable Diffusion 2.x.

Of all corporate functions, sales by its very nature is surely the most people-focused. While it may no longer involve quite as much face-to-face interaction as it once did, selling has remained emphatically a job for people rather than machines. However, artificial intelligence (AI) and machine-learning are already starting to make major inroads into the sales process, adding an extra dimension to everything from marketing automation to customer relationship management. According to Salesforce Research, high-performing teams are at least twice as likely to be using intelligent sales technologies such as artificial intelligence, sentiment analysis, next-step analysis and deep-learning. So, what further changes in the sales environment can we expect to see over the coming years?

Most organizations today are struggling with how to advance in their use of data (see our recent article on Why Your AI Project is Going to Fail). The good news is that it's straightforward (and doesn't require huge investments in tech and lots of new tech hires) to use data to grow your team's capabilities and deliver more value to your employees. The first step is to recognize where you stand today in your journey to AI maturity and that's exactly what this first tool in the Data to AI Playbookshows you. Let's look at the five stages of the Data to AI Maturity journey. We consistently hear stories from sales management about their frustrations with manual data processing.

Our cutthroat digital age has started to implement AI applications in our everyday lives, various industries; and businesses, and sales are no exception. The way how AI is changing sales activities and strategies, affecting customer relations and developing partnerships between sales representatives and AI, recently became a fundamental buzz in the industry. Research shows that using intelligent technologies in the sales reporting practice can improve forecasting, one of the daunting challenges of working in this industry. When thinking about the potential that artificial intelligence can offer to this industry, AI can surely become one of the main resources for developing a 21-century sales strategy. Here is an overview of how AI can improve sales processes, and provide exceptional results.

Today's business environment is becoming increasingly competitive. As a result, sales organizations need deeper insights and access to data to stay ahead of the competition. One way sales teams are getting a competitive advantage is through artificial intelligence and machine learning (AI/ML). However, while many companies are looking into and adopting AI/ML technologies, success relies on more than just the algorithms within the tools. Organizations need the right data in order for AI/ML to "learn" to be truly effective.

"AI is becoming more than just mainstream," says a new study from sales acceleration platform InsideSales.com. Out today, "State of Artificial Intelligence for Sales & Marketing/2018 Report" (free, registration required) shows "most people in the industry understand that AI truly enhances a sales rep's role and makes it more efficient," VP of Marketing and report co-author Gabe Larsen told me via email. "But AI isn't a stand-in for humans," he noted. "People need to sell to people, and people want to buy from people they like." Only nine percent of respondents in the company's new survey believe that AI will replace sales reps in the next five years.